Reducing data-driven dynamical subgrid scale models by physical constraints

A Discrete Dynamical System Subgrid-Scale Model of Turbulent Convection

A velocity-estimation subgrid model constrained by subgrid scale dissipation

Purely dissipative eddy–viscosity subgrid models have proven very successful in large–eddy simulations (LES) at moderate resolution. Simulations at coarse resolutions where the underlying assumption of small–scale universality is not valid, warrant more advanced models. However, non-eddy viscosity models are often unstable due to the lack of sufficient dissipation. This paper proposes a simple ...

A data-driven method for the stochastic parametrisation of subgrid-scale tropical convective area fraction

Georg A. Gottwald a ∗ and Karsten Peters b,d∗ and Laura Davies c School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia; ARC Centre of Excellence for Climate System Science, School of Mathematical Sciences, Monash University, Clayton, VIC 3800, Australia; School of Earth Sciences, University of Melbourne,VIC 3010, Australia; now at: Max Planck Institute for Meteorology,...

Data-driven stochastic parametrization for subgrid-scale tropical convective area fraction

Error bounds for data-driven models of dynamical systems

This work provides a technique for estimating error bounds about the predictions of data-driven models of dynamical systems. The bootstrap technique is applied to predictions from a set of dynamical system models, rather than from the time-series data, to estimate the reliability (in the form of prediction intervals) for each prediction. The technique is illustrated using human core temperature...